PythonModelExportSamples/LinearRegressionLearn.py at master · FlinkML/PythonModelExportSamples · GitHub

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importtensorflowastf
importnumpy
importmatplotlib.pyplotasplt

fromtensorflow.python.toolsimportfreeze_graph

# Example from https://blog.altoros.com/using-linear-regression-in-tensorflow.html
# As a training set for the tutorial, we use house prices in Portland, Oregon,
# where X (the predictor variable) is the house size and Y (the criterion variable) is the house price.
# The data set contains 47 examples.

# exporting is based on https://medium.com/@hamedmp/exporting-trained-tensorflow-models-to-c-the-right-way-cf24b609d183#.rhjmkrdln
# and https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph_test.py

# Train a data set

size_data=numpy.asarray([ 2104, 1600, 2400, 1416, 3000, 1985, 1534, 1427,
1380, 1494, 1940, 2000, 1890, 4478, 1268, 2300,
1320, 1236, 2609, 3031, 1767, 1888, 1604, 1962,
3890, 1100, 1458, 2526, 2200, 2637, 1839, 1000,
2040, 3137, 1811, 1437, 1239, 2132, 4215, 2162,
1664, 2238, 2567, 1200, 852, 1852, 1203 ])
price_data=numpy.asarray([ 399900, 329900, 369000, 232000, 539900, 299900, 314900, 198999,
212000, 242500, 239999, 347000, 329999, 699900, 259900, 449900,
299900, 199900, 499998, 599000, 252900, 255000, 242900, 259900,
573900, 249900, 464500, 469000, 475000, 299900, 349900, 169900,
314900, 579900, 285900, 249900, 229900, 345000, 549000, 287000,
368500, 329900, 314000, 299000, 179900, 299900, 239500 ])

# Test a data set

size_data_test=numpy.asarray([ 1600, 1494, 1236, 1100, 3137, 2238 ])
price_data_test=numpy.asarray([ 329900, 242500, 199900, 249900, 579900, 329900 ])

# Normalizing your data helps to improve the performance of gradient descent, especially in the case of multivariate linear regression.
defnormalize(array):
return (array-array.mean()) /array.std()

# Normalize a data set

size_data_n=normalize(size_data)
price_data_n=normalize(price_data)

size_data_test_n=normalize(size_data_test)
price_data_test_n=normalize(price_data_test)

checkpoint_path="models/saved_checkpoint"
checkpoint_state_name="saved_checkpoint"


# Display a plot
# plt.plot(size_data, price_data, 'ro', label='Samples data')
# plt.legend()
# plt.draw()

samples_number=price_data_n.size

# TF graph input
X=tf.placeholder("float")
Y=tf.placeholder("float")

# Create a model

# Set model weights
W=tf.Variable(numpy.random.randn(), name="weight")
b=tf.Variable(numpy.random.randn(), name="bias")

# Set parameters
learning_rate=0.1
training_iteration=200

# Construct a linear model
model=tf.add(tf.multiply(X, W), b, name="model")

# Minimize squared errors
cost_function=tf.reduce_sum(tf.pow(model-Y, 2))/(2*samples_number) #L2 loss
optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function, name='optimizer') #Gradient descent

# Initialize variables
init=tf.initialize_all_variables()

# 'Saver' op to save and restore all the variables
saver=tf.train.Saver()

# Launch a graph
withtf.Session() assess:
sess.run(init)

display_step=20

# Fit all training data
foriterationinrange(training_iteration):
for (x, y) inzip(size_data_n, price_data_n):
sess.run(optimizer, feed_dict={X: x, Y: y})

# Display logs per iteration step
ifiteration%display_step==0:
print"Iteration:", '%04d'% (iteration+1), "cost=", "{:.9f}".format(sess.run(cost_function, feed_dict={X:size_data_n, Y:price_data_n})), \
"W=", sess.run(W), "b=", sess.run(b)

tuning_cost=sess.run(cost_function, feed_dict={X: normalize(size_data_n), Y: normalize(price_data_n)})

print"Tuning completed:", "cost=", "{:.9f}".format(tuning_cost), "W=", sess.run(W), "b=", sess.run(b)

# Validate a tuning model

testing_cost=sess.run(cost_function, feed_dict={X: size_data_test_n, Y: price_data_test_n})

print"Testing data cost:" , testing_cost

# Display a plot
# plt.figure()
# plt.plot(size_data_n, price_data_n, 'ro', label='Normalized samples')
# plt.plot(size_data_test_n, price_data_test_n, 'go', label='Normalized testing samples')
# plt.plot(size_data_n, sess.run(W) * size_data_n + sess.run(b), label='Fitted line')
# plt.legend()

# plt.show()

# Save model weights and graph to disk
save_path=saver.save(sess, checkpoint_path, global_step=0, latest_filename=checkpoint_state_name)
print"Save path:" , save_path
graph_path=tf.train.write_graph(sess.graph_def, 'models/', 'graph.pb', as_text=True)
print"Graph path:" , graph_path

# Merge grapt with weights
# input_saver_def_path = ""
# input_binary = False
# output_node_names = "weight/read"
# restore_op_name = "save/restore_all"
# filename_tensor_name = "save/Const:0"
# clear_devices = False
# output_graph_path = "models/graphwithdata.pb"

# freeze_graph.freeze_graph(graph_path, input_saver_def_path,
# input_binary, save_path, output_node_names,
# filename_tensor_name, filename_tensor_name,
# output_graph_path, clear_devices, "")